通过数字疗法 (DTx) 重组护理服务:一个机器学习增强的评估和开发 (M-LEAD) 框架
Alessandro Carrera1, Stefania Manetti2, Emanuele Lettieri2
1School of Management, Politecnico di Milano, Milan, Italy. alessandro.carrera@polimi.it.
BMC health services research
|February 23, 2024
概括
本研究介绍了M-LEAD,一个使用机器学习指导数字治疗 (DTx) 开发和评估的框架. 它提供了创建有效DTx研究的最佳实践,并支持制造商和决策者.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 计算医学是一种计算医学.
背景情况:
- 数字化转型正在通过数字治疗 (DTx) 等创新技术重塑医疗保健.
- DTx提供以患者为中心,以数据为导向的疾病管理方法,改善结果并支持临床医生.
- 整合不断发展的数字技术对于医疗保健的进步至关重要.
研究的目的:
- 开发一个全面的框架,用于创建和评估数字治疗.
- 建立DTx开发和评估的最佳实践.
主要方法:
- 采用混合方法方法,通过系统文献审查分析了338项关于DTx的研究.
- 用描述性统计和机器学习算法来识别DTx数据中的模式.
- 该框架通过专家意见进行了质量验证.
主要成果:
- 开发了M-LEAD (机器学习增强评估和开发) 框架.
- M-LEAD建议基于其特点,监管背景和目标设计DTx研究的最佳实践.
- 该框架对研究设计提出了具体的建议.
结论:
- 该框架是DTx开发和评估标准化指南的基本步骤.
- 这些发现可以帮助DTx制造商优化产品,并为决策者提供信息.
- 它支持基于证据的DTx在医疗保健中的整合.
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